4_PLSC_grad_GSR_combat
Ju-Chi.Yu
11/2/2022
Grabbing SPINS gradients
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## Loading required package: ExPosition## Warning: package 'plotly' was built under R version 4.1.3##
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## combine## Warning: package 'MKinfer' was built under R version 4.1.3## Warning: package 'tableone' was built under R version 4.1.3## Warning: package 'RColorBrewer' was built under R version 4.1.3Read in the SPINS big table
## New names:
## Rows: 164640 Columns: 8
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (4): ROI, Network, Subject, Site dbl (4): ...1, grad1, grad2, grad3
## i Use `spec()` to retrieve the full column specification for this data. i
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## * `` -> `...1`read subject data
## [1] "record_id" "scanner"
## [3] "diagnostic_group" "demo_sex"
## [5] "demo_age_study_entry" "scog_rmet_total"
## [7] "scog_er40_total" "scog_tasit1_total"
## [9] "scog_tasit2_sinc" "scog_tasit2_simpsar"
## [11] "scog_tasit2_parsar" "scog_tasit3_lie"
## [13] "scog_tasit3_sar" "np_domain_tscore_process_speed"
## [15] "np_domain_tscore_att_vigilance" "np_domain_tscore_work_mem"
## [17] "np_domain_tscore_verbal_learning" "np_domain_tscore_visual_learning"
## [19] "np_domain_tscore_reasoning_ps"## New names:
## Rows: 467 Columns: 43
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (4): record_id, scanner, diagnostic_group, demo_sex dbl (36): ...1,
## demo_age_study_entry, scog_rmet_total, scog_er40_total, scog... lgl (3):
## exclude_MRI, exclude_meanFD, exclude_earlyTerm
## i Use `spec()` to retrieve the full column specification for this data. i
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## * `` -> `...1`Check subject overlap
grad.sub <- spins_grads_wide$Subject[order(spins_grads_wide$Subject)]
behav.sub <- lol_spins_behav$record_id[order(lol_spins_behav$record_id)]
# behav.sub[behav.sub %in% grad.sub == FALSE]
# grad.sub[grad.sub %in% behav.sub == FALSE]
# complete.cases(spins_grads_wide)
# complete.cases(lol_spins_behav)
kept.sub <- lol_spins_behav$record_id[complete.cases(lol_spins_behav)==TRUE] # 420
## grab the matching data
behav.dat <- lol_spins_behav[kept.sub,c(6:19)]
spins_grads_wide_org <- spins_grads_wide[,-1]
rownames(spins_grads_wide_org) <- spins_grads_wide$Subject
grad.dat <- spins_grads_wide_org[kept.sub,]
## variables to regress out
regout.dat <- var2regout_num[kept.sub,]Demographics
behav_all <- lol_spins_behav[kept.sub,]
table_one <- CreateTableOne(vars = colnames(behav_all)[4:19], strata="diagnostic_group",data=behav_all)
lol_demo <-
read_csv('../data/spins_lolivers_subject_info_for_grads_2022-04-21(withcomposite).csv') %>%
filter(exclude_MRI==FALSE,
exclude_meanFD==FALSE,
exclude_earlyTerm==FALSE) %>% as.data.frame## New names:
## Rows: 467 Columns: 46
## -- Column specification
## -------------------------------------------------------- Delimiter: "," chr
## (4): record_id, scanner, diagnostic_group, demo_sex dbl (39): ...1,
## demo_age_study_entry, scog_rmet_total, scog_er40_total, scog... lgl (3):
## exclude_MRI, exclude_meanFD, exclude_earlyTerm
## i Use `spec()` to retrieve the full column specification for this data. i
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## * `` -> `...1`lol_demo$subject <- sub("SPN01_", "sub-", lol_demo$record_id) %>% sub("_", "", .)
rownames(lol_demo) <- lol_demo$record_id
lol_demo_match <- lol_demo[kept.sub,]
spins_demo <- lol_demo_match %>%
select(demo_sex, demo_age_study_entry, diagnostic_group, scog_rmet_total, scog_er40_total, #scog_mean_ea,
scog_tasit1_total,
scog_tasit2_sinc,
scog_tasit2_simpsar,
scog_tasit2_parsar,
scog_tasit3_lie,
scog_tasit3_sar,
np_domain_tscore_att_vigilance,
np_domain_tscore_process_speed,
np_domain_tscore_work_mem,
np_domain_tscore_verbal_learning,
np_domain_tscore_visual_learning,
np_domain_tscore_reasoning_ps,
#bsfs_sec2_total, bsfs_sec3_total, bsfs_sec3_total, bsfs_sec4_total, bsfs_sec5_total, bsfs_sec6_total,
#fd_mean_rest
) %>% data.frame
colnames(spins_demo)## [1] "demo_sex" "demo_age_study_entry"
## [3] "diagnostic_group" "scog_rmet_total"
## [5] "scog_er40_total" "scog_tasit1_total"
## [7] "scog_tasit2_sinc" "scog_tasit2_simpsar"
## [9] "scog_tasit2_parsar" "scog_tasit3_lie"
## [11] "scog_tasit3_sar" "np_domain_tscore_att_vigilance"
## [13] "np_domain_tscore_process_speed" "np_domain_tscore_work_mem"
## [15] "np_domain_tscore_verbal_learning" "np_domain_tscore_visual_learning"
## [17] "np_domain_tscore_reasoning_ps"rownames(spins_demo) <- lol_demo_match$subject
## Statistic testing
demo_summary <- spins_demo %>%
select(-demo_sex, demo_age_study_entry) %>%
gather(key = variable, value = value, -diagnostic_group) %>%
group_by(diagnostic_group, variable) %>%
summarise(value = list(value)) %>%
spread(diagnostic_group, value) %>%
group_by(variable) %>%
mutate(
mean_ssd = mean(unlist(case), na.rm = TRUE),
mean_control = mean(unlist(control), na.rm = TRUE),
sd_ssd = sd(unlist(case), na.rm = TRUE),
sd_control = sd(unlist(control), na.rm = TRUE),
var_equal_p = var.test(unlist(case), unlist(control))$p.value,
norm_ssd = shapiro.test(unlist(case))$p.value,
norm_control = shapiro.test(unlist(control))$p.value) %>%
mutate(method = if_else(norm_ssd > .05 | norm_control > .05,
"boot", if_else(var_equal_p > .05, "welch", "student"))) %>%
mutate(
t_value = case_when(
method == "boot" ~ 999,
method == "welch" ~ t.test(unlist(case), unlist(control), var.equal = FALSE)$statistic,
method == "student" ~ t.test(unlist(case), unlist(control), var.equal = TRUE)$statistic),
df = case_when(
method == "boot" ~ 999,
method == "welch" ~ t.test(unlist(case), unlist(control), var.equal = FALSE)$parameter,
method == "student" ~ t.test(unlist(case), unlist(control), var.equal = TRUE)$parameter),
p_value = case_when(
method == "boot" ~ boot.t.test(unlist(case), unlist(control))$boot.p.value,
method == "welch" ~ t.test(unlist(case), unlist(control), var.equal = FALSE)$p.value,
method == "student" ~ t.test(unlist(case), unlist(control), var.equal = TRUE)$p.value))## `summarise()` has grouped output by 'diagnostic_group'. You can override using
## the `.groups` argument.demo_summary$p_FDR <- p.adjust(demo_summary$p_value, method = "fdr")
demo_summary## # A tibble: 15 x 15
## # Groups: variable [15]
## variable case control mean_~1 mean_~2 sd_ssd sd_co~3 var_eq~4 norm_ssd
## <chr> <lis> <list> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 demo_age_stud~ <dbl> <dbl> 31.4 31.9 9.77 10.4 3.71e- 1 1.02e- 9
## 2 np_domain_tsc~ <dbl> <dbl> 39.5 47.6 11.7 12.7 2.16e- 1 4.28e- 3
## 3 np_domain_tsc~ <dbl> <dbl> 39.7 53.1 13.2 10.1 2.33e- 4 2.89e- 1
## 4 np_domain_tsc~ <dbl> <dbl> 42.9 48.8 11.0 9.54 5.11e- 2 1.59e- 3
## 5 np_domain_tsc~ <dbl> <dbl> 40.7 50.3 8.94 9.44 4.32e- 1 7.04e- 4
## 6 np_domain_tsc~ <dbl> <dbl> 38.7 48.4 12.5 10.1 2.93e- 3 2.68e- 2
## 7 np_domain_tsc~ <dbl> <dbl> 41.3 49.2 11.2 11.4 8.26e- 1 3.88e- 1
## 8 scog_er40_tot~ <dbl> <dbl> 31.8 33.5 4.55 3.32 1.45e- 5 1.47e-13
## 9 scog_rmet_tot~ <dbl> <dbl> 24.6 27.6 5.26 3.82 1.12e- 5 7.25e- 7
## 10 scog_tasit1_t~ <dbl> <dbl> 22.5 24.6 3.64 2.14 6.71e-13 1.79e-10
## 11 scog_tasit2_p~ <dbl> <dbl> 15.7 18.5 3.95 2.09 0 3.94e-12
## 12 scog_tasit2_s~ <dbl> <dbl> 14.9 18.5 4.94 1.92 0 4.55e-13
## 13 scog_tasit2_s~ <dbl> <dbl> 16.9 17.5 3.19 2.69 1.77e- 2 1.06e-14
## 14 scog_tasit3_l~ <dbl> <dbl> 24.8 27.2 4.12 3.64 8.62e- 2 1.25e- 6
## 15 scog_tasit3_s~ <dbl> <dbl> 23.5 27.5 5.15 3.62 1.44e- 6 1.27e- 6
## # ... with 6 more variables: norm_control <dbl>, method <chr>, t_value <dbl>,
## # df <dbl>, p_value <dbl>, p_FDR <dbl>, and abbreviated variable names
## # 1: mean_ssd, 2: mean_control, 3: sd_control, 4: var_equal_plol_original$scanner %>% table## .
## CMH CMP MRC MRP ZHH ZHP
## 135 30 66 79 42 98Regress out the effects
table(regout.dat$demo_sex_num)##
## 0 1
## 159 261behav.reg <- apply(behav.dat, 2, function(x) lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)$residual)
grad.reg <- apply(grad.dat, 2, function(x) lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)$residual)
grad.reg2plot <- apply(grad.dat, 2, function(x){
model <- lm(x~regout.dat$demo_sex + regout.dat$demo_age_study_entry + regout.dat$fd_mean_rest)
return(model$residual + model$coefficient[1])
} )grab some network colours
networks <- read_delim("../networks.txt",
"\t", escape_double = FALSE, trim_ws = TRUE) %>%
select(NETWORK, NETWORKKEY, RED, GREEN, BLUE, ALPHA) %>%
distinct() %>%
add_row(NETWORK = "Subcortical", NETWORKKEY = 13, RED = 0, GREEN=0, BLUE=0, ALPHA=255) %>%
mutate(hex = rgb(RED, GREEN, BLUE, maxColorValue = 255)) %>%
arrange(NETWORKKEY)## Rows: 718 Columns: 12
## -- Column specification --------------------------------------------------------
## Delimiter: "\t"
## chr (4): LABEL, HEMISPHERE, NETWORK, GLASSERLABELNAME
## dbl (8): INDEX, KEYVALUE, RED, GREEN, BLUE, ALPHA, NETWORKKEY, NETWORKSORTED...
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.networks$hex <- darken(networks$hex, 0.2)
# oi <- networks$hex
# swatchplot(
# "-40%" = lighten(oi, 0.4),
# "-20%" = lighten(oi, 0.2),
# " 0%" = oi,
# " 20%" = darken(oi, 0.2),
# " 25%" = darken(oi, 0.25),
# " 30%" = darken(oi, 0.3),
# " 35%" = darken(oi, 0.35),
# off = c(0, 0)
# )
# networksget row and column designs
## match ROIs to networks
ROI.network.match <- cbind(spins_grads$ROI, spins_grads$Network) %>% unique
ROI.idx <- ROI.network.match[,2]
names(ROI.idx) <- ROI.network.match[,1]
### match networks with colors
net.col.idx <- networks$hex
names(net.col.idx) <- networks$NETWORK
## design matrix for subjects
sub.dx <- spins_dx_org[kept.sub,]
diagnostic.dx <- sub.dx$diagnostic_group %>% as.matrix
diagnostic.dx <- recode(diagnostic.dx, !!!c("case" = "SSD"))
diagnostic.col.idx <- c("SSD" = "darkorchid3",
"control" = "gray50")
diagnostic.col <- list()
diagnostic.col$oc <- recode(diagnostic.dx, !!!diagnostic.col.idx) %>% as.matrix()
diagnostic.col$gc <- diagnostic.col.idx %>% as.matrix
## design matrix for columns - behavioral
behav.dx <- matrix(nrow = ncol(behav.dat), ncol = 1, dimnames = list(colnames(behav.dat), "type")) %>% as.data.frame
behav.col <- c("scog" = "#D97614",#"#F28E2B",
"np" = "#3F7538",#"#59A14F",
"bsfs" = "#D37295")
behav.dx$type <- sub("(^[^_]+).*", "\\1", colnames(behav.dat))
behav.dx$type.col <- recode(behav.dx$type, !!!behav.col)
## design matrix for columns - gradient
grad.dx <- matrix(nrow = ncol(grad.dat), ncol = 4, dimnames = list(colnames(grad.dat), c("gradient", "ROI", "network", "network.col"))) %>% as.data.frame
grad.dx$gradient <- sub("(^[^.]+).*", "\\1", colnames(grad.dat))
grad.dx$ROI <- sub("^[^.]+.(*)", "\\1", colnames(grad.dat))
grad.dx$network <- recode(grad.dx$ROI, !!!ROI.idx)
grad.dx$network.col <- recode(grad.dx$network, !!!net.col.idx)
## get different alpha for gradients
grad.col.idx <- c("grad1" = "grey30",
"grad2" = "grey60",
"grad3" = "grey90")
grad.dx$gradient.col <- recode(grad.dx$gradient, !!!grad.col.idx)
## for heatmap
col.heat <- colorRampPalette(c("red", "white", "blue"))(256)Run PLS-C
pls.res <- tepPLS(behav.reg, grad.reg, DESIGN = sub.dx$diagnostic_group, make_design_nominal = TRUE, graphs = FALSE)
pls.boot <- data4PCCAR::Boot4PLSC(behav.reg, grad.reg, scale1 = "SS1", scale2 = "SS1", nIter = 1000, nf2keep = 5, eig = TRUE)## Registered S3 method overwritten by 'data4PCCAR':
## method from
## print.str_colorsOfMusic PTCA4CATApls.boot$bootRatiosSignificant.j[abs(pls.boot$bootRatios.j) < 2.88] <- FALSE
pls.boot$bootRatiosSignificant.i[abs(pls.boot$bootRatios.i) < 2.88] <- FALSE
pls.inf <- data4PCCAR::perm4PLSC(behav.reg, grad.reg, scale1 = "SS1", scale2 = "SS1", nIter = 1000)
# ## swith direction for dimension 3
pls.res$TExPosition.Data$fi[,1] <- pls.res$TExPosition.Data$fi[,1]*-1
pls.res$TExPosition.Data$fj[,1] <- pls.res$TExPosition.Data$fj[,1]*-1
pls.res$TExPosition.Data$pdq$p[,1] <- pls.res$TExPosition.Data$pdq$p[,1]*-1
pls.res$TExPosition.Data$pdq$q[,1] <- pls.res$TExPosition.Data$pdq$q[,1]*-1
pls.res$TExPosition.Data$lx[,1] <- pls.res$TExPosition.Data$lx[,1]*-1
pls.res$TExPosition.Data$ly[,1] <- pls.res$TExPosition.Data$ly[,1]*-1
## Scree plot
PlotScree(pls.res$TExPosition.Data$eigs,
p.ev = pls.inf$pEigenvalues)
## Print singular values
pls.res$TExPosition.Data$pdq$Dv## [1] 7.1330565 2.1157103 1.9079739 1.6310932 1.5321817 1.4132437 1.2515730
## [8] 1.1846931 1.1177672 1.0190911 0.9319565 0.9106204 0.8248812 0.7818378## Print eigenvalues
pls.res$TExPosition.Data$eigs## [1] 50.8804950 4.4762303 3.6403644 2.6604652 2.3475808 1.9972578
## [7] 1.5664349 1.4034979 1.2494036 1.0385467 0.8685430 0.8292296
## [13] 0.6804289 0.6112703pls.res$TExPosition.Data$t## [1] 68.5261515 6.0286134 4.9028643 3.5831302 3.1617357 2.6899186
## [7] 2.1096838 1.8902392 1.6827041 1.3987209 1.1697588 1.1168113
## [13] 0.9164057 0.8232624## Compare the inertia to the largest possible inertia
sum(cor(behav.dat, grad.dat)^2)## [1] 81.59259sum(cor(behav.dat, grad.dat)^2)/(ncol(behav.dat)*ncol(grad.dat))## [1] 0.004955818Here, we show that the effect that PLSC decomposes is pretty small to begin with. The effect size of the correlation between the two tables is 92.40 which accounts for 0.0065 of the largest possible effect.
Results
Dimension 1
lxly.out[[1]]
lx1.ssd <- pls.res$TExPosition.Data$lx[which(sub.dx$diagnostic_group == "case"), 1]
lx1.hc <- pls.res$TExPosition.Data$lx[which(sub.dx$diagnostic_group == "control"), 1]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,1],
threshold = 0,
color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,1] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")## Warning: Vectorized input to `element_text()` is not officially supported.
## i Results may be unexpected or may change in future versions of ggplot2.cor.heat <- pls.res$TExPosition.Data$X %>% heatmap(col = col.heat)
## control
grad.dat.ctrl <- grad.dat[sub.dx$diagnostic_group == "control",]
behav.dat.ctrl <- behav.dat[sub.dx$diagnostic_group == "control",]
corX.ctrl <- cor(as.matrix(behav.dat.ctrl),as.matrix(grad.dat.ctrl))
heatmap(corX.ctrl[cor.heat$rowInd, cor.heat$colInd], col = col.heat, Rowv = NA, Colv = NA)
## case
grad.dat.case <- grad.dat[sub.dx$diagnostic_group == "case",]
behav.dat.case <- behav.dat[sub.dx$diagnostic_group == "case",]
corX.case <- cor(as.matrix(behav.dat.case),as.matrix(grad.dat.case))
heatmap(corX.case[cor.heat$rowInd, cor.heat$colInd], col = col.heat, Rowv = NA, Colv = NA)
Bootstrap confidence intervals on the first LV
\[CV_{control} = \] 1.04 % bootstrap CI: [0.81, 1.35]
\[CV_{SSD} = \] 2.34 % bootstrap CI: [1.83, 3.12]
Dimension 2
lxly.out[[2]]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,2],
threshold = 0, color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,2] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
dim1.est <- pls.res$TExPosition.Data$pdq$Dv[1]*as.matrix(pls.res$TExPosition.Data$pdq$p[,1], ncol = 1) %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1], ncol = 1))
cor.heat.res1 <- (pls.res$TExPosition.Data$X - dim1.est) %>% heatmap(col = col.heat)
Dimension 3
lxly.out[[3]]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,3],
threshold = 0, color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,3] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
dim2.est <- (as.matrix(pls.res$TExPosition.Data$pdq$p[,1:2]) %*% pls.res$TExPosition.Data$pdq$Dd[1:2,1:2] %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1:2])))
cor.heat.res2 <- heatmap(pls.res$TExPosition.Data$X - dim2.est, col = col.heat)
Dimension 4
lxly.out[[4]]
gridExtra::grid.arrange(bar.grad1, bar.grad2, bar.grad3, ncol = 1)
PrettyBarPlot2(pls.res$TExPosition.Data$fi[,4],
threshold = 0,
color4bar = ifelse(pls.boot$bootRatiosSignificant.i[,4] == TRUE, behav.dx$type.col, "grey90"),
horizontal = FALSE, main = "Scores - behavioural")
dim3.est <- (as.matrix(pls.res$TExPosition.Data$pdq$p[,1:3]) %*% pls.res$TExPosition.Data$pdq$Dd[1:3,1:3] %*% t(as.matrix(pls.res$TExPosition.Data$pdq$q[,1:3])))
cor.heat.res3 <- heatmap(pls.res$TExPosition.Data$X - dim3.est, col = col.heat)
Back into the brain
Dimension 1
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
Dimension 2
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
Dimension 3
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
Dimension 4
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
Group difference and fancy figures
Cohen’s
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
Group difference
visualization
Group effect
By contributions and significance to PLS
## merging atlas and data by 'label'## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## merging atlas and data by 'label'
##
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
## merging atlas and data by 'label'
Subcortical
Group effect
By contributions and significance to PLS
Merge figures
## merging atlas and data by 'label'
## merging atlas and data by 'label'
## merging atlas and data by 'label'
Relationship to symptoms
Correlation to composite scores
## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
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Correlation to individual scores
